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Roy, C. & Kovordányi, R. (2018). Tropical Cyclone Track Forecasting. In: Darius Bartlett; Ramesh P. Singh (Ed.), Exploring Natural Hazards: A Case Study Approach. Taylor & Francis
Open this publication in new window or tab >>Tropical Cyclone Track Forecasting
2018 (English)In: Exploring Natural Hazards: A Case Study Approach / [ed] Darius Bartlett; Ramesh P. Singh, Taylor & Francis, 2018Chapter in book (Refereed)
Abstract [en]

Tropical cyclones are large-scale low-pressure systems that form over warm tropical and subtropical waters. These low-pressure systems are characterized by high-speed surface wind circulation, rotating spirals of thick clouds, heavy rain, and surges, the water masses sometimes reaching a height of 10 meters when they hit a coastline. Tropical cyclones are one of the most destructive meteorological disasters due to their high damaging power, both through strong winds and flooding. To minimize economic loss and to save human lives, meteorologists have developed a range of techniques for forecasting tropical cyclone track. The most common techniques utilize statistical and mathematical equations to integrate the movement pattern of historical tropical cyclones with the recently observed movement of the current tropical cyclone. Alternatively, forecasting techniques can focus on the forces responsible for tropical cyclone motion to produce a cyclone track forecast. Today, improved cyclone track forecasting techniques have enabled meteorological offices to warn residents in the affected areas before a tropical cyclone impact, and help to reduce the losses created by them.

Place, publisher, year, edition, pages
Taylor & Francis, 2018
Keywords
Tropical cyclone motion, track prediction techniques, track prediction accuracy
National Category
Meteorology and Atmospheric Sciences
Identifiers
urn:nbn:se:liu:diva-123197 (URN)10.1201/9781315166858 (DOI)9781315166858 (ISBN)
Available from: 2015-12-06 Created: 2015-12-06 Last updated: 2019-07-02
Roy, C. & Kovordanyi, R. (2015). The current cyclone early warning system in Bangladesh: Providers' and receivers' views. International Journal of Disaster Risk Reduction, 12, 285-299
Open this publication in new window or tab >>The current cyclone early warning system in Bangladesh: Providers' and receivers' views
2015 (English)In: International Journal of Disaster Risk Reduction, E-ISSN 2212-4209, Vol. 12, p. 285-299Article in journal (Refereed) Published
Abstract [en]

Bangladesh has experienced several catastrophic Tropical Cyclones (TCs) during the last decades. Despite the efforts of disaster management organizations, as well as the Bangladesh Meteorological Department (BMD), there were lapses in the residents’ evacuation behavior. To examine the processes of TC forecasting and warning at BMD and to understand the reasons for residents’ reluctance to evacuate after a cyclone warning, we conducted an individual in-depth interview among the meteorologists at BMD, as well as a questionnaire survey among the residents living in the coastal areas. The results reveal that the forecasts produced by BMD are not reliable for longer than 12-h. Therefore, longer-term warnings have to be based on gross estimates of TC intensity and motion, which renders the disseminated warning messages unreliable. Our results indicate that residents in the coastal areas studied, do not follow the evacuation orders due to mistrust of the warning messages—which can deter from early evacuation; and insufficient number of shelters and poor transportation possibilities—which discourages late evacuation. Suggestions made by the residents highlight the necessity of improved warning messages in the future. These findings indicate the need for improved forecasting, and more reliable and more informative warning messages for ensuring a timely evacuation response from residents.

Place, publisher, year, edition, pages
Elsevier, 2015
Keywords
Accurate tropical cyclone forecasting; Informative warning message; Warning message interpretation; Meteorologists’ perspective; Residents’ perspective; Principal component analysis
National Category
Social Sciences Interdisciplinary
Identifiers
urn:nbn:se:liu:diva-117927 (URN)10.1016/j.ijdrr.2015.02.004 (DOI)000357735000026 ()
Available from: 2015-05-18 Created: 2015-05-18 Last updated: 2019-07-05Bibliographically approved
Kovordanyi, R. & Eriksson, H. (2013). Advanced Decision Support in Simulator-Based Training for Crisis Management. In: : . Paper presented at National Symposium on Technology and Methodology for Security and Crisis Management, TAMSEC.
Open this publication in new window or tab >>Advanced Decision Support in Simulator-Based Training for Crisis Management
2013 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

This paper describes the development of a decision support and knowledge management system as part of an EU FP7 funded project, CRISIS. In its final form, the decision support and knowledge management system was implemented as an Android app that uses Esper for complex event processing. The reasoning engine of the decision support and knowledge management system is backed with an ontology and knowledge representation implemented in Topic Maps.

National Category
Computer Systems
Identifiers
urn:nbn:se:liu:diva-117926 (URN)
Conference
National Symposium on Technology and Methodology for Security and Crisis Management, TAMSEC
Projects
CRISIS
Funder
EU, FP7, Seventh Framework Programme, FP7-242474
Available from: 2015-05-18 Created: 2015-05-18 Last updated: 2019-07-05Bibliographically approved
Rankin, A., Field, J., Kovordanyi, R. & Eriksson, H. (2012). Instructor’s Tasks in Crisis Management Training. In: Proceedings of the 9th International ISCRAM Conference, 2012. Paper presented at 9th International Conference on Information Systems for Crisis Response Management (ISCRAM 2012), April 22-25, Vancover, Canada.
Open this publication in new window or tab >>Instructor’s Tasks in Crisis Management Training
2012 (English)In: Proceedings of the 9th International ISCRAM Conference, 2012, 2012Conference paper, Published paper (Other academic)
Abstract [en]

In crisis management exercises the instructor’s performance is critical to the success of the training. It is their responsibility to monitor and evaluate the exercise, as well as appropriately adjust and adapt the scenario to the unfolding events. Despite the importance of the instructor’s skills in crisis management training little has been documented regarding successful methods or common pitfalls. The study presented in this paper is exploratory and aimed at investigating how instructors monitor and control large scale crisis management exercises. The results are intended to be used as a basis for further investigation on how instructors can be supported in virtual reality training systems. A summary of results from interviews is presented and followed by observations reports from two live exercises. Finally, key areas for instructor support in virtual-reality training systems are identified.

Keywords
crisis management, exercise management, instructor support, training, training systems
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:liu:diva-80330 (URN)
Conference
9th International Conference on Information Systems for Crisis Response Management (ISCRAM 2012), April 22-25, Vancover, Canada
Available from: 2012-08-23 Created: 2012-08-23 Last updated: 2018-01-12
Kovordanyi, R., Pelfrene, J., Rankin, A., Schreiner, R., Jenvald, J., Morin, M. & Eriksson, H. (2012). Real-time Support for Exercise Managers’ Situation Assessment and Decision Making. Paper presented at ISCRAM2012.
Open this publication in new window or tab >>Real-time Support for Exercise Managers’ Situation Assessment and Decision Making
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2012 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

Exercise managers and instructors have a particularly challenging task in monitoring and controlling on-going exercises, which may involve multiple response teams and organizations in highly complex and continuously evolving crisis situations. Managers and instructors must handle potentially incomplete and conflicting field-observation data and make decisions in real-time in order to control the flow of the exercise and to keep it in line with the training objectives. In simulation-based exercises, managers and instructors have access to a rich set of real-time data, with an increased potential to closely monitor the trainees’ actions, and to keep the exercise on track. To assist exercise managers and instructors, data about the on-going exercise can be filtered, aggregated and refined by real-time decision-support systems. We have developed a model and a prototype decision-support system, using stream-based reasoning to assist exercise managers and instructors in real-time. The approach takes advantage of topic maps for ontological representation and a complex-event processing engine for analyzing the data stream from a virtual-reality simulator for crisis-management training. Aggregated data is presented both on-screen, in Twitter, and in the form of topic maps.

Keywords
Crisis management, decision making, stream-based reasoning, complex-event processing, semantic event processing, ontologies, topic maps
National Category
Interaction Technologies
Identifiers
urn:nbn:se:liu:diva-80333 (URN)
Conference
ISCRAM2012
Projects
CRISIS - Critical Incident management training System using an Interactive Simulation environmenr
Funder
EU, European Research Council, FP7-242474
Available from: 2012-08-28 Created: 2012-08-23 Last updated: 2014-11-28Bibliographically approved
Roy, C. & Kovordanyi, R. (2012). Tropical cyclone track forecasting techniques: A review. Atmospheric research, 104-105, 40-69
Open this publication in new window or tab >>Tropical cyclone track forecasting techniques: A review
2012 (English)In: Atmospheric research, ISSN 0169-8095, E-ISSN 1873-2895, Vol. 104-105, p. 40-69Article, review/survey (Refereed) Published
Abstract [en]

Delivering accurate cyclone forecasts in time is of key importance when it comes to saving human lives and reducing economic loss. Difficulties arise because the geographical and climatological characteristics of the various cyclone formation basins are not similar, which entail that a single forecasting technique cannot yield reliable performance in all ocean basins. For this reason, global forecasting techniques need to be applied together with basin-specific techniques to increase the forecast accuracy. As cyclone track is governed by a range of factors variations in weather conditions, wind pressure, sea surface temperature, air temperature, ocean currents, and the earths rotational force-the coriolis force, it is a formidable task to combine these parameters and produce reliable and accurate forecasts. In recent years, the availability of suitable data has increased and more advanced forecasting techniques have been developed, in addition to old techniques having been modified. In particular, artificial neural network based techniques are now being considered at meteorological offices. This new technique uses freely available satellite images as input, can be run on standard PCs, and can produce forecasts with good accuracy. For these reasons, artificial neural network based techniques seem especially suited for developing countries which have limited capacity to forecast cyclones and where human casualties are the highest. © 2011 Elsevier B.V.

Place, publisher, year, edition, pages
Elsevier, 2012
Keywords
Artificial neural networks; Cyclone forecasting models; Cyclone forecasting techniques; Cyclone track forecasting; Hurricane; Typhoon
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-74115 (URN)10.1016/j.atmosres.2011.09.012 (DOI)
Available from: 2012-01-19 Created: 2012-01-19 Last updated: 2017-12-08Bibliographically approved
Saifullah, M. & Kovordányi, R. (2011). Emergence of Attention Focus in a Biologically-Based Bidirectionally-Connected Hierarchical Network. In: Andrej Dobnikar, Uroš Lotrič, Branko Šter (Ed.), Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part I (pp. 200-209). Springer Berlin/Heidelberg
Open this publication in new window or tab >>Emergence of Attention Focus in a Biologically-Based Bidirectionally-Connected Hierarchical Network
2011 (English)In: Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part I / [ed] Andrej Dobnikar, Uroš Lotrič, Branko Šter, Springer Berlin/Heidelberg, 2011, p. 200-209Chapter in book (Refereed)
Abstract [en]

We present a computational model for visual processing where attentional focus emerges fundamental mechanisms inherent to human vision. Through detailed analysis of activation development in the network we demonstrate how normal interaction between top-down and bottom-up processing and intrinsic mutual competition within processing units can give rise to attentional focus. The model includes both spatial and object-based attention, which are computed simultaneously, and can mutually reinforce each other. We show how a non-salient location and a corresponding non-salient feature set that are at first weakly activated by visual input can be reinforced by top-down feedback signals (centrally controlled attention), and instigate a change in attentional focus to the weak object. One application of this model is highlight a task-relevant object in a cluttered visual environment, even when this object is nonsalient (non-conspicuous).

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2011
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 6593
Keywords
Spatial attention, Object-based attention, Biased competition, Recurrent bidirectionally connected, networks
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-85047 (URN)10.1007/978-3-642-20282-7_21 (DOI)978-3-642-20281-0 (ISBN)978-3-642-20282-7 (ISBN)
Available from: 2012-11-06 Created: 2012-10-31 Last updated: 2018-01-31Bibliographically approved
Rankin, A., Field, J., Kovordanyi, R., Morin, M., Jenvald, J. & Eriksson, H. (2011). Training Systems Design: Bridging The Gap Between User and Developers Using Storyboards. In: Proceedings of the 29th Annual European Conference on Cognitive Ergonomics. Paper presented at ECCE 2011: 29th Annual European Conference on Cognitive Ergonomics (pp. 205-212).
Open this publication in new window or tab >>Training Systems Design: Bridging The Gap Between User and Developers Using Storyboards
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2011 (English)In: Proceedings of the 29th Annual European Conference on Cognitive Ergonomics, 2011, p. 205-212Conference paper, Published paper (Other academic)
Abstract [en]

Motivation -- Designing distributed training systems for crisis management (CM) requires an approach with the ability to address a great variety of needs and goals. Crisis responses involve multiple agents, each with different backgrounds, tasks, priorities, goals, responsibilities, organizations, equipment, and approaches. Identifying the different user training needs and translating these into user and functional requirement therefore poses great challenges.

Research approach -- In this paper we present experiences of how to enable the collaboration between multiple stakeholders and partners when creating and adapting ideas throughout the design phase. The techniques have been used in a European project aimed at developing an interactive Virtual Reality (VR) environment for training crisis management.

Findings/Design -- The focus of the paper is on the initial storyboard iterations and lo-fi prototypes, as this is a crucial stage for expressing ideas in a perceivable way without having to spend too much time and effort on creating detailed prototypes.

Take away message -- Experiences using low-cost commercial software for creating storyboards are presented, as these provided the means to create, share, present, adapt and circulate ideas, facilitating the fusing of ideas, shared understanding and distributed working.

National Category
Human Computer Interaction
Identifiers
urn:nbn:se:liu:diva-80331 (URN)10.1145/2074712.2074752 (DOI)978-1-4503-1029-1 (ISBN)
Conference
ECCE 2011: 29th Annual European Conference on Cognitive Ergonomics
Available from: 2012-08-23 Created: 2012-08-23 Last updated: 2018-01-12
Saifullah, M., Kovordanyi, R. & Roy, C. (2010). Bidirectional Hierarchical Neural Networks: Hebbian Learning Improves Generalization. In: Proceedings of the Fifth International Conference on Computer Vision Theory and Applications,  Volume 1: . Paper presented at Fifth International Conference on Computer Vision Theory and Applications (VISAPP'10), May 17-21, 2010, Angers, France (pp. 105-111).
Open this publication in new window or tab >>Bidirectional Hierarchical Neural Networks: Hebbian Learning Improves Generalization
2010 (English)In: Proceedings of the Fifth International Conference on Computer Vision Theory and Applications,  Volume 1, 2010, p. 105-111Conference paper, Published paper (Other academic)
Abstract [en]

Visual pattern recognition is a complex problem, and it has proven difficult to achieve satisfactorily instandard three-layer feed-forward artificial neural networks. For this reason, an increasing number ofresearchers are using networks whose architecture resembles the human visual system. These biologicallybasednetworks are bidirectionally connected, use receptive fields, and have a hierarchical structure, withthe input layer being the largest layer, and consecutive layers getting increasingly smaller. These networksare large and complex, and therefore run a risk of getting overfitted during learning, especially if smalltraining sets are used, and if the input patterns are noisy. Many data sets, such as, for example, handwrittencharacters, are intrinsically noisy. The problem of overfitting is aggravated by the tendency of error-drivenlearning in large networks to treat all variations in the noisy input as significant. However, there is one wayto balance off this tendency to overfit, and that is to use a mixture of learning algorithms. In this study, weran systematic tests on handwritten character recognition, where we compared generalization performanceusing a mixture of Hebbian learning and error-driven learning with generalization performance using pureerror-driven learning. Our results indicate that injecting even a small amount of Hebbian learning, 0.01 %,significantly improves the generalization performance of the network.

Keywords
generalization, image processing, bidirectional hierarchical neural networks, Hebbian learning, feature extraction, object recognition
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-77026 (URN)10.5220/0002835501050111 (DOI)978-989-674-028-3 (ISBN)
Conference
Fifth International Conference on Computer Vision Theory and Applications (VISAPP'10), May 17-21, 2010, Angers, France
Available from: 2012-08-28 Created: 2012-05-02 Last updated: 2016-01-13Bibliographically approved
Eriksson, H., Kovordanyi, R. & Rankin, A. (2010). CRISIS: Virtual-reality-based training for emergency management. Paper presented at TAMSEC.
Open this publication in new window or tab >>CRISIS: Virtual-reality-based training for emergency management
2010 (English)Conference paper, Published paper (Refereed)
National Category
Interaction Technologies
Identifiers
urn:nbn:se:liu:diva-80354 (URN)
Conference
TAMSEC
Available from: 2012-08-28 Created: 2012-08-23 Last updated: 2014-11-28Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2801-7050

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